3 resultados para 3D sensor

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The biomagnetic techniques use different magnetic field detectors to measure parameters of the human physiology. Those techniques present the advantage of being noninvasive and radiation free. Among them we can show up the Superconducting Quantum Interference Device (SQUID), the Current Alternate Biosusceptometry (ACB) and, more recently, the employment of anisotropic magnetoresistive sensors. Those magnetic sensors have a low cost and good sensitivity to measure different physiological parameters using magnetic markers. The biomagnetic techniques have being used successfully through study on the characteristics of the gastrointestinal tract. Recent research, the magnetoresistors were used to evaluate the transit time and localization of magnetic sources in different parts of the gastrointestinal tract. The objective of this work is the characterization, with in vitro tests, of a biomagnetic instrumentation using two 3-axis magnetoresistors arranged in a gradiometric coplanar setup to evaluate esophageal transit time, analyze and compare the results of experimental signals and the magnetic theory, as well as evaluate the instrumentation gain with use of tri-axial sensor front to the mono-axial sensor. The instrumentation is composed by two three-axis sensing magnetometers, precision power supply and amplifier electronic circuits. The sensors fixed in a coplanar setup were separate by distance of 18 cm. The sensitivity tests had been carried through using a cylindrical magnet (ø = 4 mm and h = 4 mm) of neodymium-iron-boron (grid 35). The tests were done moving the permanent magnet on the sensors parallel axis, simulating the food transit in... (Complete abstract click electronic access below)

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In this work is presented a new method for sensor deployment on 3D surfaces. The method was structured on different steps. The first one aimed discretizes the relief of interest with Delaunay algorithm. The tetrahedra and relative values (spatial coordinates of each vertex and faces) were input to construction of 3D Voronoi diagram. Each circumcenter was calculated as a candidate position for a sensor node: the corresponding circular coverage area was calculated based on a radius r. The r value can be adjusted to simulate different kinds of sensors. The Dijkstra algorithm and a selection method were applied to eliminate candidate positions with overlapped coverage areas or beyond of surface of interest. Performance evaluations measures were defined using coverage area and communication as criteria. The results were relevant, once the mean coverage rate achieved on three different surfaces were among 91% and 100%.